Efficacy and safety of driver‐guided catheter ablation for atrial fibrillation: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Targeting localized drivers (electrical rotors or focal impulses) during catheter ablation for atrial fibrillation (AF) has been proposed as a strategy to improve procedural success. However, the strength and quality of the evidence to support this approach is unclear. METHODS AND RESULTS: Clinical studies reporting efficacy or safety outcomes of driver-guided ablation for AF were identified in Medline, Embase, the Cochrane Central Register of Controlled Trials, the Cochrane Database of Systematic Reviews, Pubmed, and conference abstracts from major scientific meetings. Random-effects meta-analysis of efficacy outcomes from controlled studies was performed. Thirty-one reports from 30 studies were included: two randomized controlled trials, five nonrandomized controlled studies, and 23 uncontrolled studies. In controlled studies, driver-guided ablation has been associated with higher rates of acute AF termination (RR 2.08, 95% CI 1.43-3.05; P < 0.001) and increased freedom from AF/atrial tachycardia (AT) at ≥1 year (RR 1.34, 95% CI 1.05-1.70; P = 0.02). Similar rates of procedural complications have been reported between ablation strategies. Overall, current data on driver-guided ablation are predominantly from nonrandomized studies with considerable heterogeneity in mapping and ablation strategies used and in clinical outcomes reported. CONCLUSION: Pooled data on the efficacy of AF driver-guided catheter ablation suggest increased freedom from AF/AT relative to conventional strategies. However, most studies are nonrandomized and of moderate quality. Though promising data exist, there remains no conclusive evidence for the efficacy of AF driver ablation. Robust data from randomized trials are needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".